对抗域适应促进跨异构RNA-seq数据集的知识转移
Adversarial Domain Adaptation Enables Knowledge Transfer Across Heterogeneous RNA-Seq Datasets
AI总结：
本研究提出基于深度学习的域适应框架，通过跨异构RNA-seq数据集实现有效知识转移，提升癌症类型分类的准确性，尤其在低数据场景下表现更优。
摘要：
从RNA测序（RNA-seq）数据准确预测表型对于诊断、生物标志物发现和个性化医学至关重要。深度学习模型已显示出比传统机器学习方法表现更优的潜力，但其性能依赖于大量标注良好的数据集。在转录组学中，此类数据集经常受限，导致过拟合和泛化能力差。从较大、更通用的数据集进行知识转移可以缓解这一问题。然而，跨RNA-seq数据集转移信息仍然具有挑战性，因为存在异构预处理流程和目标表型的差异。在本研究中，我们提出了一种基于深度学习的域适应框架，旨在有效从大型通用数据集向小型数据集转移知识以进行癌症类型分类。该方法通过联合优化分类和域对齐目标学习域不变的潜在空间。为了确保训练稳定性和数据稀缺场景的鲁棒性，该框架采用对抗方法并辅以适当的正则化。既探索了监督和非监督方法变体，利用标注或未标注的目标样本。该框架在三个大规模转录组学数据集（TCGA、ARCHS4、GTEx）上进行评估，以评估其在不同群体间转移知识的能力。实验结果表明，与非适应基线相比，在低数据情况下，癌症和组织类型分类的准确性有持续提升。总体而言，本工作强调域适应作为转录组学中数据高效知识转移的强大策略，使在受限数据条件下实现稳健的表型预测成为可能。
英文摘要：
Accurate phenotype prediction from RNA sequencing (RNA-seq) data is essential for diagnosis, biomarker discovery, and personalized medicine. Deep learning models have demonstrated strong potential to outperform classical machine learning approaches, but their performance relies on large, well-annotated datasets. In transcriptomics, such datasets are frequently limited, leading to over-fitting and poor generalization. Knowledge transfer from larger, more general datasets can alleviate this issue. However, transferring information across RNA-seq datasets remains challenging due to heterogeneous preprocessing pipelines and differences in target phenotypes. In this study, we propose a deep learning-based domain adaptation framework that enables effective knowledge transfer from a large general dataset to a smaller one for cancer type classification. The method learns a domain-invariant latent space by jointly optimizing classification and domain alignment objectives. To ensure stable training and robustness in data-scarce scenarios, the framework is trained with an adversarial approach with appropriate regularization. Both supervised and unsupervised approach variants are explored, leveraging labeled or unlabeled target samples. The framework is evaluated on three large-scale transcriptomic datasets (TCGA, ARCHS4, GTEx) to assess its ability to transfer knowledge across cohorts. Experimental results demonstrate consistent improvements in cancer and tissue type classification accuracy compared to non-adaptive baselines, particularly in low-data scenarios. Overall, this work highlights domain adaptation as a powerful strategy for data-efficient knowledge transfer in transcriptomics, enabling robust phenotype prediction under constrained data conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
